The detection paradox: How COVID-19 surveillance patterns shifted from lower-income to more affluent neighborhoods in Miami-Dade County
7 affiliations
- 1Fort Lauderdale Research and Education Center, School of Forest, Fisheries and Geomatics Sciences, University of Florida, Davie, Florida, United States of America.
- 2Department of Medical Education, Herbert Wertheim College of Medicine, Florida International University, Miami, Florida, United States of America.
- 3Department of Medical Education, Dr. Kiran C. Patel College of Allopathic Medicine (NSU MD), Nova Southeastern University, Fort Lauderdale, Florida, United States of America.
- 4Office of Clinical Affairs, Herbert Wertheim College of Medicine, Florida International University, Miami, Florida, United States of America.
- 5Lee Health, Fort Myers, Florida, United States of America.
- 6Bureau of Epidemiology, Florida Department of Health, Tallahassee, Florida, United States of America.
- 7Department of Family Medicine and Community Health, Herbert Wertheim College of Medicine, Florida International University, Miami, Florida, United States of America.
Abstract
Passive disease surveillance systems may systematically undercount burden in low-resource minority neighborhoods, yet fine-scale evidence of this phenomenon remains limited. We examined spatiotemporal patterns of reported COVID-19 cases across Miami-Dade County's diverse neighborhoods and investigated which socioeconomic and demographic characteristics predicted areas of elevated and suppressed case rates. In an ecological, cross-sectional analysis of 491,769 geocoded cases across 1,831 census block groups from March 2020 to May 2021, we applied negative binomial regression with spatial eigenvector filtering and Getis-Ord Gi* hot spot analysis across five pandemic periods aligned with major policy shifts. The analysis revealed a detection paradox. Early hot spots concentrated in lower-income census block groups (median household income: 52,022–55,676), but by September 2020, hot spots had shifted to more affluent areas (median: $77,069). Historically Black neighborhoods along the I-95 corridor emerged as persistent cold spots despite multiple structural risk factors: lower incomes, reduced insurance coverage, and limited healthcare access. From Period 3 onward, predominantly Black census block groups showed significantly lower reported rates (incidence rate ratio [IRR]: 0.991–0.995, p < 0.001). Household overcrowding was tested but retained in no final model. These paradoxical patterns suggest that as testing expanded, surveillance increasingly tracked healthcare utilization rather than transmission. What appeared as neighborhood “protection” likely reflected barriers to testing access shaped by transportation constraints, work schedule inflexibility, and historical relationships with medical institutions. Census block group analysis revealed these dynamics obscured at broader geographic scales. The findings challenge the assumption that reported case rates approximate true disease burden and have direct implications for emergency response planning: surveillance systems require complementary detection streams, risk-adjusted monitoring for underdetection, and targeted outreach where poverty and limited access to testing are concentrated but reported rates are low.
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BibTeX
@article{juhasz2026detection,
title = {{The detection paradox: How COVID-19 surveillance patterns shifted from lower-income to more affluent neighborhoods in Miami-Dade County}},
author = {Levente Juhász and Nana Aisha Garba and Imelda K Moise and Prasad B Bhoite and Christopher Clark and Rachel D Clarke and Nicole Kikuchi Stuver and David R Brown},
journal = {PLOS One},
year = {2026},
month = oct,
doi = {10.1371/journal.pone.0361211},
url = {https://doi.org/10.1371/journal.pone.0361211},
note = {CC BY},
}
RIS
TY - JOUR
TI - The detection paradox: How COVID-19 surveillance patterns shifted from lower-income to more affluent neighborhoods in Miami-Dade County
AU - Levente Juhász
AU - Nana Aisha Garba
AU - Imelda K Moise
AU - Prasad B Bhoite
AU - Christopher Clark
AU - Rachel D Clarke
AU - Nicole Kikuchi Stuver
AU - David R Brown
PY - 2026
DA - 2026/10/23
JO - PLOS One
DO - 10.1371/journal.pone.0361211
UR - https://doi.org/10.1371/journal.pone.0361211
AB - Passive disease surveillance systems may systematically undercount burden in low-resource minority neighborhoods, yet fine-scale evidence of this phenomenon remains limited. We examined spatiotemporal patterns of reported COVID-19 cases across Miami-Dade County's diverse neighborhoods and investigated which socioeconomic and demographic characteristics predicted areas of elevated and suppressed case rates. In an ecological, cross-sectional analysis of 491,769 geocoded cases across 1,831 census block groups from March 2020 to May 2021, we applied negative binomial regression with spatial eigenvector filtering and Getis-Ord Gi* hot spot analysis across five pandemic periods aligned with major policy shifts. The analysis revealed a detection paradox. Early hot spots concentrated in lower-income census block groups (median household income: 52,022–55,676), but by September 2020, hot spots had shifted to more affluent areas (median: $77,069). Historically Black neighborhoods along the I-95 corridor emerged as persistent cold spots despite multiple structural risk factors: lower incomes, reduced insurance coverage, and limited healthcare access. From Period 3 onward, predominantly Black census block groups showed significantly lower reported rates (incidence rate ratio [IRR]: 0.991–0.995, p < 0.001). Household overcrowding was tested but retained in no final model. These paradoxical patterns suggest that as testing expanded, surveillance increasingly tracked healthcare utilization rather than transmission. What appeared as neighborhood “protection” likely reflected barriers to testing access shaped by transportation constraints, work schedule inflexibility, and historical relationships with medical institutions. Census block group analysis revealed these dynamics obscured at broader geographic scales. The findings challenge the assumption that reported case rates approximate true disease burden and have direct implications for emergency response planning: surveillance systems require complementary detection streams, risk-adjusted monitoring for underdetection, and targeted outreach where poverty and limited access to testing are concentrated but reported rates are low.
SN - 1932-6203
ER -